Analysis of HMM-Based Lombard Speech Synthesis

Humans modify their voice in interfering noise in order to maintain the intelligibility of their speech – this is called the Lombard effect. This ability, however, has not been extensively modeled in speech synthesis. Here we compare several methods of synthesizing speech in noise using a physiologically based statistical speech synthesis system (GlottHMM). The results show that in a realistic street noise situation the synthetic Lombard speech is judged by listeners both as appropriate for the situation and as intelligible as natural Lombard speech. Of the different types of models, one using adaptation and extrapolation performed the best.